Original Title: “Three Frameworks for Ordinary People to Achieve AI Capability Leap”
Original Author: KK.aWSB, Co-founder of CarbonSilicon AI

There are two types of people using AI: One opens Claude every day, inputs a long background description, gets an answer, then closes the page. The next day, they do the same thing again. After 30 days, their efficiency is exactly the same as on the first day.

The other type also uses Claude, but after 30 days, their AI has become a completely different thing—automatically writing in their tone, outputting in their format, calling their taught methodologies automatically. And they spend less and less time “guiding AI” each day.

Same tool, same model, same price. How does the gap happen?

It’s not a skill difference. It’s a cognitive framework difference.

Today, I’ll share three frameworks. Understanding them will fundamentally change how you use AI.

Framework 1: The Three-Level Evolution Theory—Which Level Are You On?

Using AI has three levels. Most people will stay at the first level forever.

Level 1: Prompt

Prompt is the instruction you temporarily input in the chat box. “You are a senior copywriter,” “Use a concise style,” “Give me three options.”

It’s effective in the moment. When the session ends, it disappears.

It’s like explaining who you are to a forgetful genius every morning. It’s smart, but tomorrow it won’t remember you. Your tone preferences, brand standards, output formats, industry terminology—all reset, all need to be re-explained.

What does it look like after 30 days? Day 1 writes a good prompt and gets good results. Day 15, you’ve repeated the same context about 15 times. Day 30, your productivity is exactly the same as on Day 1. Zero accumulation.

And on tired days, you might miss details, and the output quality drops. On busy days, you skip context altogether, and Claude gives you a generic version.

You are the bottleneck. Every conversation is.

Level 2: Project

You upload reference documents, style guides, system instructions into a Project. Every dialogue within this Project knows your context.

It’s like giving a new employee an onboarding manual. Much better than just explaining orally every day.

But there’s a problem: you must remember to open the correct Project. Your knowledge is locked inside a specific Project; switch scenarios, and you start from scratch.

Level 3: Skill

Skill is a structured file—you write it once, install it once, and afterward, Claude automatically triggers it when relevant tasks appear.

No need to open a specific Project. No need to input prompts each time. Claude just knows what to do.

It’s like training an employee: once trained, it’s forever effective.

The three levels use the same Claude. But the first is a chat tool, the third is a work system.

So, after understanding this layering, how do you jump from Level 1 to Level 3? That’s where the second framework comes in.

Framework 2: Transactional Thinking vs. Compound Interest Thinking

This is the most important of the three frameworks. It’s not a tool usage tip but a cognitive model.

Prompt is a transaction. You invest time to write an instruction, get an output once. Next time, invest again, get another output. The input-output relationship is linear 1:1. Stop investing, and the output immediately drops to zero.

Skill is compound interest. On Day 1, you spend 10 minutes creating a Skill; by Day 2, it’s already working for you. By Day 15, you’ve accumulated 3 Skills, each stacking on the previous. By Day 30, your Claude is different from everyone else’s.

The setup cost is an hour of scattered effort in the first week. The returns are that every subsequent dialogue runs on a higher baseline.

Work done in the first week still yields returns after six months. That’s compound interest.

Transactional thinkers ask daily: “How can I use AI to do this well today?”

Compound interest thinkers ask: “How can I make AI always know how to do this?”

A tiny difference in words. But if you use compound interest thinking with AI, after 30 days, you’ll find something magical: the time spent “teaching AI” decreases, while the work AI completes for you increases. Because every Skill you’ve taught continues to be effective.

This raises a practical question: How exactly should Skill be written? What to include, what to omit? That’s the third framework.

Framework 3: Thin Harness, Fat Skills—Focus 90% of your effort on the right things

This framework comes from YC’s Garry Tan, who distilled it into an extremely concise architectural principle: Thin Harness, Fat Skills.

What does it mean?

When working with AI, you’re actually building a three-layer system—whether you realize it or not:

Top layer: Skills. The operation manual you teach AI—processes, judgment standards, domain knowledge. This accounts for 90% of the value.

Middle layer: Harness. The program or environment that runs AI—calling models, managing context, reading and writing files. Keep it extremely thin.

Bottom layer: Deterministic tools. Database queries, code compilation, mathematical calculations—operations that produce the same output from the same input every time.

The principle is: Push intelligence into Skills. Push execution into deterministic tools. The thinner the Harness, the better.

What’s the anti-pattern? Thick Harness, thin Skills. You’ve seen cases where people spend a lot of time debugging toolchains, configuring plugins, optimizing API calls, but haven’t written a single line about “how to do this well” for AI.

The result: the toolchain looks great, but the AI output quality is no different from plain chatting. Because you’ve optimized the pipeline, but what flows through it is still tap water.

The model’s intelligence is already sufficient. Its failure isn’t due to lack of intelligence but because it doesn’t understand your specific situation—your standards, routines, the unique shape of your problems. Skills solve this.

Another key inference from this framework: When a more powerful model is released, all your Skills automatically improve.

Because Skills define processes and standards, and improvements in underlying judgment make these processes more precise. You don’t need to rewrite anything. Model upgrades aren’t “relearning” for you—they’re “system upgrades for free.”

Skills are permanent assets.

How to connect these three frameworks?

Step 1: Use the Three-Level Evolution Theory to locate yourself.

Where are you now? If every conversation involves re-inputting context—you’re at Level 1. If you’re using Projects but without Skills—you’re at Level 2. Knowing where you stand helps you decide where to go next.

Step 2: Use compound interest thinking to find your Skill candidates.

Reflect on your AI conversations over the past month. Which instructions have you repeated? Which contexts have you explained repeatedly? Which formatting requirements do you have to remind every time? Which processes have you manually guided step-by-step?

If you repeat something more than three times, it’s a Skill waiting to be created.

There’s an even more aggressive principle: if you ask AI to do something, and you’ll do it again in the future—turn that into a Skill on the first try. Manually do it once, check the output, and if satisfied, encode it into a Skill file immediately.

Evaluation criterion: If you need to ask for the same thing a second time, the system has failed.

Step 3: Use Thin Harness, Fat Skills to decide where to focus your effort.

Don’t spend three days debugging the toolchain and then run tasks with plain prompts. Instead—spend three days writing your core Skills, and keep the toolchain as simple as possible.

What does a Skill look like? It’s extremely simple—a text file:

Name—what it’s called. Description—what it does (one sentence). This is the most critical part—Claude relies on this sentence to decide when to trigger automatically. Instruction—how to do it (specific steps). Constraints—what not to do.

Skills aren’t about telling AI “what to do”—that’s Prompt’s job. Skills tell AI “how to do it.”

Prompt says: “Help me write a competitor analysis.” Skill says: “When doing competitor analysis, first identify 3-5 core competitors, compare by features/price/market positioning, output in SWOT format, include data sources for each conclusion, and finally give 3 actionable recommendations.”

Prompt provides the task. Skill provides the methodology. When combined, AI shifts from “an intern waiting for instructions” to “an employee who knows how to work.”

And the same Skill can be repeatedly invoked with different inputs—input a competitor company, get a competitor analysis; input an industry trend, get a trend report; input an investment target, get a due diligence brief. The same process, different objects, entirely different outputs.

This isn’t Prompt engineering. It’s software design with Markdown.

How to build your first Skill?

The fastest way: let AI help you build it.

Claude has a built-in “Skill Creator”—a Skill that creates Skills. Just say: “Help me create a Skill for [your specific task].”

Claude will interview you, extract the process, and output a structured .md file. Save it, and you’re ready to use.

In an afternoon, you can set up your entire Skill system. Each takes 10 to 15 minutes. Writing style, competitor analysis, meeting minutes, email replies, report generation, content calendar—less than two hours total.

This two-hour compound interest has no upper limit.

Final thoughts

Three frameworks, three sentences:

Three-Level Evolution Theory: From Prompt to Project to Skill, the same AI offers three completely different experiences. Which level are you on?

Transactional vs. Compound Interest: Prompt is a daily reset transaction. Skill is a perpetual asset. Which do you choose?

Thin Harness, Fat Skills: Don’t spend effort on the toolchain. Focus 90% of your attention on writing good Skills—that’s where the value is.

Every Skill you build is a permanent upgrade to your AI system. It doesn’t degrade, forgets nothing, and automatically gets stronger with model updates.

Prompt is a verbal command. Skill is an SOP manual. One resets daily, the other compounds daily.

Starting today: find the task you repeat more than three times. Spend 10 minutes writing your first Skill.

And you’ll never want to go back to just using Prompts again.

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